In [1]:
import numpy as np
from scipy.stats import kurtosis, skew
import pandas as pd
import math
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
df = pd.read_csv('aug 4 2018.csv', header=None)
df.columns = ['seq1', 'start1', 'end1', 'res1', 'chain1', 'pdb1', 'seq2', 'start2', 'end2', 'res2', 'chain2', 'pdb2', 'rms', 'occ' , 'count']
In [3]:
df.head()
Out[3]:
In [4]:
id_rms_df = df.groupby(['seq1'])['rms'].apply(list).to_frame()
id_rms_df = id_rms_df.reset_index()
In [5]:
num_bins = np.arange(0, 3.1, 0.1)
# bins
x_x = np.arange(0, 3.1, 0.1)
y_y = np.arange(0, 30, 10)
# y = [0, 10, 20, 30, 40, 50, 60]
In [6]:
# example data
for index, row in id_rms_df.iterrows():
mu = np.array(row['rms']).mean() # mean of distribution
sigma = np.array(row['rms']).std() # standard deviation of distribution
fig, ax = plt.subplots(figsize=(10,4))
# the histogram of the data
n, bins, patches = ax.hist(row['rms'], num_bins, density=1, edgecolor='black', linewidth=1.2)
# add a 'best fit' line
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')
ax.set_xlabel(' -- RMS -- ')
ax.set_ylabel(' -- No of fragments -- ')
ax.set_title('RMS Distribution of ' + row['seq1'])
ax.text(2.3, 15, 'Sequence: ' + row['seq1'], style='italic',
bbox={'facecolor':'red', 'alpha': 0.5, 'pad':10})
ax.text(2.3, 10, 'Skew: ' + str(skew(row['rms']))[:8], style='italic',
bbox={'facecolor':'yellow', 'alpha': 0.5, 'pad':10})
ax.text(2.3, 5, 'Kurtosis: ' + str(kurtosis(row['rms']))[:8], style='italic',
bbox={'facecolor':'lightblue', 'alpha': 0.5, 'pad':10})
ax.set_xticks(x_x)
ax.set_yticks(y_y)
# Tweak spacing to prevent clipping of ylabel
fig.tight_layout()
plt.show()
# print('Sequence: ' + row['seq'])
# print('Skew: ', skew(row['RMS Value']))
# print('Kurtosis: ', kurtosis(row['RMS Value']))
